{"id":"W4416725824","doi":"10.1109/igarss55030.2025.11244052","title":"Underwater Sensing of Ship-Radiated Noise Based on Interpretable Deep Learning and Acoustic Feature Fusion","year":2025,"lang":"","type":"article","venue":"","topic":"Underwater Acoustics Research","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; Memorial University of Newfoundland","funders":"Fundamental Research Funds for the Central Universities","keywords":"Noise (video); Feature (linguistics); Underwater; Pattern recognition (psychology); Deep learning; Noise reduction; Channel (broadcasting); Hydrophone; SIGNAL (programming language)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005404346,0.000675191,0.0004996441,0.0006791205,0.0001798335,0.0005164079,0.0006612716,0.000592193,0.000611676],"category_scores_gemma":[0.001369659,0.0002191989,0.0006388126,0.0005754689,0.0003777713,0.0008933942,0.001213307,0.0007555629,0.0001958485],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003273355,"about_ca_system_score_gemma":0.0003925207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002247968,"about_ca_topic_score_gemma":0.002771549,"domain_scores_codex":[0.9997342,0.00005111486,0.00001469408,0.00007452626,0.00007916168,0.00004631979],"domain_scores_gemma":[0.9996723,0.0001293196,0.00005398528,0.00004954455,0.00007483229,0.00002005266],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002923184,0.0002762018,0.006498236,0.0001288249,0.0001371926,0.0002564928,0.000193531,0.5403534,0.0673343,0.004139857,0.001778105,0.3786116],"study_design_scores_gemma":[0.000002818583,0.00002381695,0.000948841,0.000003426287,0.000008915721,0.0000174291,0.00001190875,0.9937926,0.003821105,0.001181369,0.0001813046,0.000006572686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1786276,0.000381591,0.8173566,0.000267985,0.00007456494,0.00003207488,0.0001666191,0.0009331785,0.002159829],"genre_scores_gemma":[0.9106921,0.0001747944,0.08732685,0.0001273165,0.00004631096,0.00004107136,0.0003846565,0.00003866681,0.001168296],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002247968,"threshold_uncertainty_score":0.004469812,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01057687060990857,"score_gpt":0.246547866106358,"score_spread":0.2359709954964495,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}